Mixed Logical Dynamic Research Articles

Research on Hybrid Logic Dynamic Model and Voltage Predictive Control of Photovoltaic Storage System

This paper investigates microgrid systems characterized by the coexistence of discrete events and continuous events, a typical hybrid system. By selecting the charging and discharging processes of the energy storage unit as logical variables, a mixed logical dynamic (MLD) model for the microgrid in islanded mode is established. Based on this model, model predictive control (MPC) theory is employed to optimize the energy management strategy, aiming to stabilize the DC bus voltage of the photovoltaic (PV) unit and minimize the switching frequency of the energy storage unit’s charging and discharging processes during system operation.

Fault‐tolerant control of current residual vector three‐phase four‐switch motor drive system based on MLD model

AbstractAiming at the problem of single‐phase switch tube open circuit fault in inverters, a current residual vector three‐phase four‐switch fault‐tolerant control strategy based on the mixed logic dynamic (MLD) model of permanent magnet synchronous motor (PMSM) drive system is proposed. Firstly, the MLD and three‐phase four‐switch mathematical models of the motor drive system are established and a calculation method of DC bus capacitance is proposed. Secondly, to improve the dynamic performance of the motor, an improved vector control based on the MLD model is proposed. Finally, by setting the threshold and dividing the area of the current residual vector, the accuracy of inverter fault diagnosis is improved, and the fast and smooth switching of the drive system after the fault is realized. Simulation and experimental results show that this strategy can effectively reduce the influence of calculation error and system noise, and has strong robustness for the change of motor speed and load.

Research on MLD Modeling and Predictive Control of Magnetically Coupled Resonant Bidirectional WPT System

The recent studies on magnetically coupled resonant bidirectional wireless power transfer (MCR-BWPT) systems disregard the challenges posed by nonlinearity, discrete switching action, and hybrid properties within the system. This research focuses on the D-LCL resonant compensation topology MCR-BWPT system. The switch tube’s switching state dictates the division of various working modes and the determination of the switching conditions between them. The coupling relationship between the continuous dynamic characteristics of the system and discrete events and the constrained conditions of the system are derived. The Hybrid System Description Language (HYSDEL) is used to build the Mixed Logic Dynamic (MLD) model of the system. The MLD model is employed as the prediction model, and the hybrid model predictive controller of the MCR-BWPT system is constructed according to the quadratic performance index. Finally, to verify the accuracy of the MLD model and the feasibility of the control strategy, the simulation model of the MLD model is established in MATLAB/Simulink. The study’s findings show that, in terms of response time at system startup and power fluctuation suppression, the approach put forward in this research performs better than both the conventional bilateral dual-phase-shift control strategy and the PQ-based bilateral power control strategy. The MCR-BWPT system can operate more steadily now that PQ’s bidirectional power control technique is in place. The system’s forward and reverse transmission efficiency is increased by 0.29% and 0.32% compared to the conventional bilateral dual-phase-shift control approach; the increases are 0.28% and 0.09%, each compared to the bilateral power control strategy based on PQ.

Hierarchical model predictive control for islanded and grid-connected microgrids with wind generation and hydrogen energy storage systems

This paper presents a novel energy management strategy (EMS) to control a wind-hydrogen microgrid which includes a wind turbine paired with a hydrogen-based energy storage system (HESS), i.e., hydrogen production, storage and re-electrification facilities, and a local load. This complies with the mini-grid use case as per the IEA-HIA Task 24 Final Report, where three different use cases and configurations of wind farms paired with HESS are proposed in order to promote the integration of wind energy into the grid. Hydrogen production surpluses by wind generation are stored and used to provide a demand-side management solution for energy supply to the local and contractual loads, both in the grid-islanded and connected modes, with corresponding different control objectives. The EMS is based on a hierarchical model predictive control (MPC) in which long-term and short-term operations are addressed. The long-term operations are managed by a high-level MPC, in which power production by wind generation and load demand forecasts are considered in combination with day-ahead market participation. Accordingly, the hydrogen production and re-electrification are scheduled so as to jointly track the load demand, maximize the revenue through electricity market participation and minimize the HESS operating costs. Instead, the management of the short-term operations is entrusted to a low-level MPC, which compensates for any deviations of the actual conditions from the forecasts and refines the power production so as to address the real-time market participation and the short time-scale equipment dynamics and constraints. Both levels also take into account operation requirements and devices’ operating ranges through appropriate constraints. The mathematical modeling relies on the mixed-logic dynamic (MLD) framework so that the various logic states and corresponding continuous dynamics of the HESS are considered. This results in a mixed-integer linear program which is solved numerically. The effectiveness of the controller is analyzed by simulations which are carried out using wind forecasts and spot prices of a wind farm in center-south of Italy.

Torsional oscillation suppression-oriented torque compensate control for regenerative braking of electric powertrain based on mixed logic dynamic model

This paper presents a novel torsional oscillation suppression-oriented torque compensate control for regenerative braking (TS-RTC) of electric powertrain, which is based on the gear-pair backlash and half-shaft flexibility-considered dynamic model of regenerative braking in electric powertrain (REP). According to the characteristics of transition from driving mode to regenerative braking mode (DM-to-RM), the dynamic models under different stages are transmitted into an equivalent mixed-logical dynamical (MLD) hybrid model. MLD-based hybrid braking system model and hybrid model predictive control are adopted to design the TS-RTC strategy, in which the torsional oscillation suppression-oriented cost function proved efficient in improving the regenerative braking performance during DM-to-RM process. Simulations and REP hardware in the loop test show that the fluctuation of half shaft torque are efficiently suppressed and the vehicle drivability is improved.

Operating Hydrogen-Based Energy Storage Systems in Wind Farms for Smooth Power Injection: A Penalty Fees Aware Model Predictive Control

Smooth power injection is one of the possible services that modern wind farms could provide in the not-so-far future, for which energy storage is required. Indeed, this is one among the three possible operations identified by the International Energy Agency (IEA)-Hydrogen Implementing Agreement (HIA) within the Task 24 final report, that may promote their integration into the main grid, in particular when paired to hydrogen-based energy storages. In general, energy storage can mitigate the inherent unpredictability of wind generation, providing that they are deployed with appropriate control algorithms. On the contrary, in the case of no storage, wind farm operations would be strongly affected, as well as their economic performances since the penalty fees wind farm owners/operators incur in case of mismatches between the contracted power and that actually delivered. This paper proposes a Model Predictive Control (MPC) algorithm that operates a Hydrogen-based Energy Storage System (HESS), consisting of one electrolyzer, one fuel cell and one tank, paired to a wind farm committed to smooth power injection into the grid. The MPC relies on Mixed-Logic Dynamic (MLD) models of the electrolyzer and the fuel cell in order to leverage their advanced features and handles appropriate cost functions in order to account for the operating costs, the potential value of hydrogen as a fuel and the penalty fee mechanism that may negatively affect the expected profits generated by the injection of smooth power. Numerical simulations are conducted by considering wind generation profiles from a real wind farm in the center-south of Italy and spot prices according to the corresponding market zone. The results show the impact of each cost term on the performances of the controller and how they can be effectively combined in order to achieve some reasonable trade-off. In particular, it is highlighted that a static choice of the corresponding weights can lead to not very effective handling of the effects given by the combination of the system conditions with the various exogenous’, while a dynamic choice may suit the purpose instead. Moreover, the simulations show that the developed models and the set-up mathematical program can be fruitfully leveraged for inferring indications on the devices’ sizing.

MLD–MPC for Ultra-Supercritical Circulating Fluidized Bed Boiler Unit Using Subspace Identification

Before carbon capture and storage technologies can truly be promoted and applied, and nuclear or renewable energy power generation can become predominant, it is important to further develop more efficient and ultra-low emission USC units on the basis of leveraging the strengths of CFB technology. In view of this complex system with strong nonlinearity such as the boiler-turbine unit of a thermal power unit, the establishment of a model that is suitable for control is indispensable for the operation and the economics of the process. In this study the form of the nonlinear model after linearization at the steady-state point has been fully considered and an improved subspace identification method, which is based on the steady-state point deviations data, was proposed in order to identify a piecewise affine model. In addition, the construction of the excitation signal in practical applications has been fully considered. The identification results demonstrate that this method has a better adaptability to strong nonlinear systems. The identification normalized root mean square errors of each working condition were almost all less than 10%. On this basis, a framework that is widely applicable to complex system control has been established by combining with the mixed logic dynamic (MLD) model. The canonical form realization was performed in order to transfer the local models into the same state basis. The predictive control was carried out on the boiler-turbine system of a 660-MW ultra-supercritical circulating fluidized bed unit that was based on the above framework. The results indicate that the predictive control performance is closely related to the setting value of the ramp rate and, therefore, prove the effectiveness of the framework.

A Hybrid Model-Based Diagnosis Approach for Open-Switch Faults in PMSM Drives

In this letter, a hybrid model-based diagnosis method is proposed for open-switch faults in permanent magnet synchronous motor drives by combining the advantages of both model predictive control (MPC) and mixed logical dynamic (MLD) models. This idea is based on the receding optimization of the MPC and the estimating capability of the MLD. The predictive current model and the MLD model are established to predict and estimate phase currents, respectively. Besides, the logical vectors are theoretically investigated under various faulty conditions. Derived from the cost function, a diagnosis function is designed to evaluate the consistency between the actual and estimated currents. Consequently, the logical vector minimizing the diagnosis function is selected to locate faulty switches. Finally, experiments are conducted to verify the effectiveness of the proposed method.

Online Fault Diagnosis Method for Grid-Connected Inverters Based on Finite-Set Mixed Logical Dynamical Model Prediction

The grid-connected inverter is a key device in the renewable energy power generation system and large-scale energy storage system, which the operational stability and reliability are the basis for the efficient and safe application of electrical energy. A real-time fault diagnosis method of a three-phase for grid-connected application combining a mixed logic dynamic (MLD) model and finite control set model predictive control (FCS-MPC) is proposed. This paper not only realizes the open circuit fault diagnosis and location of the switching devices in the main power circuit, but also discusses the threshold issues and post-fault operations. The advantage of the proposed method is that it directly uses the control data and measurement signals of the controller without extra sensors and calculation, which will shorter the fault diagnosis time and occupy less calculation resource of the main processor. Simulation results illustrate the quickness of the fault identification and accurate position with robustness to the interference of the diagnosis method. Finally, the effectiveness of the diagnosis method was verified by a 1500W experimental prototype in a laboratory.

Resilience-oriented schedule of microgrids with hybrid energy storage system using model predictive control

Microgrids can be regarded as a promising solution by which to increase the resilience of power systems in an energy paradigm based on renewable generation. Their main advantage is their ability to work as islanded systems under power grid outage conditions. Microgrids are usually integrated into electrical markets whose schedules are carried out according to economic aspects, while resilience criteria are ignored. This paper shows the development of a resilience-oriented optimization for microgrids with hybrid Energy Storage System (ESS), which is validated via numerical simulations. A hybrid ESS composed of hydrogen and batteries is, therefore, considered with the objective of improving the autonomy of the microgrid while achieving a rapid transition response. The control problem is formulated using Stochastic Model Predictive Control (SMPC) techniques in order to take into account possible transitions between grid-connected and islanded modes at all the sample instants of the schedule horizon (SH). The control problem is developed by considering a healthy operation of the hybrid ESS, thus avoiding degradation issues. The plant is modeled using the Mixed Logic Dynamic (MLD) framework, owing to the presence of logic and dynamic control variables.

An online optimal energy management strategy for a dual-mode power-split hybrid electric vehicle based on hybrid MPC Algorithm*

This paper proposed a global on-line optimisation energy management strategy based on the hybrid model predictive control (HMPC) algorithm. First, the non-linear models of a dual-mode power-split hybrid electric vehicle is built. Then, the dual-mode power-split hybrid electric vehicle is considered as a hybrid system which includes continuous and discrete states, and a mixed logical dynamic (MLD) predictive model is built based on hybrid system theory. Thirdly, an HMPC energy management strategy is designed based on the MLD predictive model. The HMPC-based energy management strategy is compared with dynamic programming based and rule-based energy management strategies. The simulation results indicate that the HMPC-based energy management strategy is helpful for real-time control and improving fuel economy. good control performance. Finally, the control performance of the proposed HMPC-based energy management strategy is compared with that of the DP-based energy management strategy and rule- based energy management strategy. The simulation results indicated that the control performance of HMPC can achieve 80.6 % of that of DP.

Optimal operations for hydrogen-based energy storage systems in wind farms via model predictive control

Efficient energy production and consumption are fundamental points for reducing carbon emissions that influence climate change. Alternative resources, such as renewable energy sources (RESs), used in electricity grids, could reduce the environmental impact. Since RESs are inherently unreliable, during the last decades the scientific community addressed research efforts to their integration with the main grid by means of properly designed energy storage systems (ESSs). In order to highlight the best performance from these hybrid systems, proper design and operations are essential. The purpose of this paper is to present a so-called model predictive controller (MPC) for the optimal operations of grid-connected wind farms with hydrogen-based ESSs and local loads. Such MPC has been designed to take into account the operating and economical costs of the ESS, the local load demand and the participation to the electricity market, and further it enforces the fulfillment of the physical and the system's dynamics constraints. The dynamics of the hydrogen-based ESS have been modeled by means of the mixed-logic dynamic (MLD) framework in order to capture different behaviors according to the possible operating modes. The purpose is to provide a controller able to cope both with all the main physical and operating constraints of a hydrogen-based storage system, including the switching among different modes such as ON, OFF, STAND-BY and, at the same time, reduce the management costs and increase the equipment lifesaving. The case study for this paper is a plant under development in the north Norway. Numerical analysis on the related plant data shows the effectiveness of the proposed strategy, which manages the plant and commits the equipment so as to preserve the given constraints and save them from unnecessary commutation cycles.

MLD-Based Thermal Behavior Analysis of Traction Converters Under Faulty Conditions

The existing thermal behavior analysis mainly focuses on the normal condition of converter but lacks investigation in the cases of faulty condition and modulation strategy switching. This further leads to inaccuracy and incomprehensive thermal management. In order to address this issue, in this article, a mixed-logical-dynamic (MLD)-based electrothermal model is proposed for a fast observation of the thermal behavior of power devices within converters under both normal case and open-circuit (OC) fault cases. It also has the advantage of applicability to various modulation strategies. To be specific, the switching-logic-mixed (SLM) and transition-logic-mixed (TLM) variables are first defined to analyze the generating conditions of power loss in power modules, where the power loss models considering the normal and OC fault cases are built, after which the MLD-based electrothermal model is established to calculate the junction temperature of power modules, which will be used to obtain the thermal behaviors of converters. Finally, under various OC fault cases, the thermal behavior of traction converters is analyzed by hardware-in-the-loop (HIL) real-time simulation test. The HIL-based results show that the proposed model can be used to analyze the thermal behaviors under various scenarios in real time, which can further offer guidance for thermal management under faulty conditions.

Cooperative Optimization of Networked Microgrids for Supporting Grid Flexibility Services Using Model Predictive Control

The transition towards fully renewable energy-based power systems will require to increase the number of reserves at the System Operators' (SOs) disposal to provide flexibility on the energy management process. The microgrid's ability of integrating distributed energy resources, loads and energy storage systems (ESS) appears as a powerful flexibility tool. Nevertheless, the associated control problem of microgrids increases with the number of connected devices. A structuration of the distribution grids in networks of microgrids is proposed, focusing on their ability to provide flexibility services. The complexity of the associated optimization algorithm is faced using Distributed Model Predictive Control (MPC). The algorithm is divided in two steps. The first one is applied to the cooperative participation of microgrids in the day-ahead market. The second step covers the interaction with the SO offering flexibility services in exchange for a financial benefit. The financial benefit is optimally shared between the networked microgrids to satisfy the power profile requested by the SO at the lowest cost. As the proposed control algorithm presents both continuous and binary variables, its associated optimization problem is formulated using the Mixed Logic Dynamic (MLD) framework, which results in a Mixed Integer Quadratic Programming problem (MIQP).

Similarity Analysis-Based Diagnosis Method for Open-Circuit Faults of Inverters in PMSM Drive Systems

A fast, real-time and similarity analysis-based diagnosis method for open-circuit faults in three-phase voltage-source inverters is proposed. The proposed method is based on the mixed logical dynamic (MLD) model of the PMSM drive system to simulate three-phase currents under different faulty conditions, and the Pearson correlation coefficients (PCCs) are used to measure the similarity between the simulated fault types and the actual one, then the fault detection and isolation can be achieved according to the characteristics of PCCs under different faulty conditions. The proposed method only needs to use the current sensors existing in the closed-loop system to measure the currents for fault diagnosis, and can detect the fault within one current fundamental wave period. Simulation results verify the effectiveness of the proposed method.

Economic Management Based on Hybrid MPC for Microgrids: A Brazilian Energy Market Solution

This paper proposes a microgrid central controller (MGCC) solution to the energy management problem of a renewable energy-based microgrid (MG). This MG is a case study from the Brazilian energy market context and, thus, has some operational particularities and rules to be obeyed. The MGCC development was based on a hybrid model predictive control (HMPC) strategy using the mixed logical dynamic (MLD) approach to deal with logical constraints within the HMPC structure, which results in a mixed integer programming (MIP) problem. The development of the solution is done through economic and dynamic modeling of the MG components; furthermore, it also takes into account the energy compensation rules of the Brazilian energy market and the white energy tariff. These conditions are specified through a set of MLD constraints. The effectiveness and performance of the proposed solution are evaluated through high-fidelity numerical simulation.

A Semi-Continuous PWA Model Based Optimal Control Method for Nonlinear Systems

To alleviate the mode mismatch of multiple model methods for nonlinear systems when completely discrete dynamical equations are adopted, a semi-continuous piecewise affine (SCPWA) model based optimal control method is proposed. Firstly, a SCPWA model is constructed where modes evolve in continuous time and continuous states evolve in discrete time. Thanks to this model, a piecewise affine (PWA) system can switch at any time instant whereas mode switching only occurs at sample instants when a completely discrete PWA model is adopted, which improves the prediction accuracy of multi-models. Secondly, the switching condition is relaxed such that operating subspaces have overlaps and switching condition parameters are introduced. As a consequence, an optimal control problem with fixed mode switching sequence is established. Finally, a SCPWA model based model predictive control (MPC) policy is designed for nonlinear systems. The convergence of the MPC algorithm is proved. Compared with widely used mixed logical dynamic (MLD) model based methods, the proposed method not only alleviates mode mismatch, but also lightens the computing burden, hence improves the control performance and reduces the computation time. Some numerical examples are provided as well to show the efficiency of the method.

A Mode Selected Mixed Logic Dynamic Model and Model Predictive Control of Buck Converter

A Mixed Logic Dynamic (MLD) model and control method based on mode selection are proposed for the Buck convertor. In establishing the hybrid system model, the factors such as the inductor current are neglected, and the Model Predictive Control (MPC) is used to switch the most favorable working state of the control target. Since the modeling process ignores the inductance and current, it is necessary to convert the optimization prediction control resulting to avoid the problem that the model is inconsistent with the control object. The method proposed in this paper uses fewer auxiliary logic variables and mixed logic variables in the modeling process, simplifying the model and improving the solution speed. This method can not only make the Buck converter work in the Continuous Current Mode (CCM) but also work in the Discontinuous Current Mode (DCM), extending the adjustment range of the Buck converter. The simulation results show that the proposed method has a better control performance than the traditional MLD model.

H-TMD with hybrid control method for vibration control of long span cable-stayed bridge

Long span cable-stayed bridges are extremely vulnerable to dynamic excitations such as which caused by traffic load, wind and earthquake. Studies on cable-stayed bridge vibration control have been keenly interested by researchers and engineers in design new bridges and assessing in-service bridges. In this paper, a novel Hybrid-Tuned Mass Damper (H-TMD) is proposed and a hybrid control model named Mixed Logic Dynamic (MLD) is employed to build the bridge-H-TMD system to mitigate the vibrations. Firstly, the fundamental theory and modeling process of MLD model is introduced. After that, a new state switching design of the H-TMD and state space equations for different states are proposed to control the bridge vibrations. As the state switching designation presented, the H-TMDs can applied active force to bridge only if the structural responses are beyond the limited thresholds, otherwise, the vibrations can be reduced by passive components of dampers without active control forces provided. A new MLD model including both passive and active control states is built based on the MLD model theory and the state switching design of H-TMD. Then, the case study is presented to demonstrate the proposed methodology. In the case study, the control scheme with H-TMDs is applied for a long span cable-stayed bridge, and the MLD model is established and simulated with earthquake excitation. The simulation results reveal that the suggested method has a well damping effect and the established system can be switched between different control states as design excellently. Finally, the energy consumptions of H-TMD schemes are compared with that of Active Tuned Mass Damper (ATMD) schemes under variable seismic wave excitations. The compared results show that the proposed H-TMD can save energy than ATMD.

Mixed Logical Dynamic Based Optimal Control Model for User Side Integrated Energy System

User side integrated energy system (IES) interconnects power distribution network, natural gas network and heat network. The complex multi-energy collaboration and hybrid dynamic poses a great challenge on optimal control strategy. In this paper, a mixed logical dynamic (MLD) model for IES is firstly established to represent energy flow at arbitrary time point as well as system’s dynamic performance over a certain time period. Then an optimal control model for IES and its rolling optimization method is proposed, which makes full use of mixed integer linear inequality constraints in the MLD model and minimize the control cost index. Finally, a numerical example of a user side IES verified the effectiveness of the proposed methodology.